Virtual colonoscopy (VC) refers to a method of diagnosis based on computer simulation of standard, minimally invasive endoscopic procedures using patient specific three-dimensional (3D) anatomic data sets. Examples of current endoscopic procedures include bronchoscopy, sinusoscopy, upper gastro-intestinal endoscopy, colonoscopy, cystoscopy, cardioscopy, and urethroscopy. VC visualization of non-invasively obtained patient specific anatomic structures avoids risks, such as perforation, infection, hemorrhage, and so forth, associated with real endoscopy, and provides the endoscopist with important information prior to performing an actual endoscopic examination. Such understanding can minimize procedural difficulties, decrease patient morbidity, enhance training and foster a better understanding of therapeutic results.
In virtual colonoscopy, 3D images are created from two-dimensional (2D) computerized tomography (CT) or magnetic resonance (MR) data, for example, by volume rendering. Volume rendering, as such, is a known technique for interpreting volume data. Present-day CT and MRI scanners typically produce a set of cross-sectional images which, in combination, produce a set of volume data. These 3D images are created to simulate images coming from an actual endoscope, such as a fiber optic endoscope.
The computerized post-processing of imaging data from cross-sectional imaging modalities is presently of importance in the field of medicine
Typically, the Volume Rendering Technique (VRT) requires rendering settings that include a classification function that defines the visibility of materials present in the data set. The classification function is also referred to herein as a transfer function or rendering setting. It is generally assumed that different materials map to different intensity levels and therefore the classification function maps intensity levels to respective opacity values. Thus, the classification function essentially determines which voxel will be rendered, and which voxel will be invisible or “transparent”; a low opacity value results in a translucent or even invisible object while a high opacity value results in a clearly visible object. See, for example, the textbook “Virtual Endoscopy and Related 3D Techniques,” edited by A. L. Baert; Springer, New York; 2001, 2002.
In many systems, this can be carried out interactively. A user can manipulate a simple transfer function such as, for example, trapezoid, and can see immediately the result. He can then adjust it until he gets the images desired. In some cases, this function can be extremely complicated, so that adjusting takes considerable time. If the relation of voxel value to tissue is known, such as for example in CT, where air, water, etc have known values, preset functions can be used. But in cases where the voxel values are not known, such as, for example, in MR, or CT with an unknown amount of contrast agent in the blood, the user has to adjust the transfer function to each individual case.
In the worst case, the user needs to adjust the transfer functions not only per case, but also depending on the location in the dataset where being looked at. An example would be virtual endoscopy of MR data. MR data is usually not homogeneous; the contrast varies at different locations. Unlike conventional 3D rendering that shows a body from the outside and therefore requires that the rendering parameters be adjusted globally, Virtual Endoscopy only shows very local parts of the body, such as, for example, the inside of a part of the colon, or the inside of an airway. Because the region that is rendered is local, the rendering parameters have to be adjusted to the local data in order to produce optimal quality.
Taosong He et al., in the article entitled Generation of Transfer Functions with Stochastic Search Techniques, IEEE 1996, propose a solution with 3 different quality criteria: maximizing first order image entropy, maximizing variance of pixels in final image, or maximizing edge energy in the final image. None of these criterions takes into account 3D features such as 3D surface normals or local properties of the input data (histogram).
Kindlmann G. L. in the article entitled Semi-Automatic Generation of Transfer Functions for Direct Volume Rendering Dissertation, Cornell University, 1999 proposes a technique to render material boundaries in volume dataset. In a first step, the data is analyzed by calculating first and second order derivatives. In a second step, the user has to select regions and decide what to render. Although this technique facilitates the creation of rendering parameters, it is not automatic in the sense that the user still has to adjust parameters and evaluate the quality visually.